Abstract
Data transmission is the most critical operation for mobile sensors networks in term of energy waste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of service also by means of energy saving policies. Communication and data transmission are among the most critical operation for such devises in term of energy waste. In this paper we present a novel approach to increase battery life-span by means of shorter transmission due to data compression. On the other hand, since this latter operation has a non-neglectable energy cost, we developed a compression efficiency estimator based on the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for the evaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication. In facts the developed technique, due to its independence from the string or file length, is extremely robust both for small and big data files, as well as to evaluate whether or not to compress data before transmission. Since the proposed solution provides a quantitative analysis of the source's entropy and the related statistics, it has been implemented as a preprocessing step before transmission. A dynamic threshold defines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatly reduce the transmission length. On the other hand a data compression algorithm should be used only when the energy gain of the reduced transmission time is presumably greater than the energy used to run the compression software. In this paper we developed an automatic evaluation system in order to optimize the data transmission in mobile sensor networks, by compressing data only when this action is presumed to be energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well as standard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensive with respect to a compression algorithm. Finally the computational complexity of the proposed approach is virtually neglectable with respect to the compression and transmission routines themselves.
Highlights
Te micro-electro-mechanical systems (MEMS) technology has encountered a tremendous evolution in the last decades [1]–[3]
In this work we developed a new approach to increase the energy data trasmission efficiency in pervasive healthcare sensor networks
In the presented approach the sensors battery life has been extended by means of a shorter communication time due to data compression
Summary
Te micro-electro-mechanical systems (MEMS) technology has encountered a tremendous evolution in the last decades [1]–[3]. As possible trade-off, it would be agreeable to transmit compressed data only when such operation greatly reduces the transmission time It follows that, for mobile senors networks communicating by means of wireless signals, data should be compressed only after a positive estimation of the compression efficiency of the data compression algorithm (see Figure 1). While lossy compression is in generally suitable for a wide range of applications, on the field of sensors and sensor networks such data requires to be perfectly reconstructed, often, only lossless compression techniques are applicable It follows that, in lossless compression, an a priori estimate of the source statistics is highly desirable since it allows us to estimate the maximum theoretically-achievable compression ratio. In all cases the observed computing time is typically one order lower than that required to compress the files with the best data compressors on the market
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